Sparse Representation with Principal Component Analysis in Face Recognition

Face recognition has been one of the most reliable biometric technologies due to its easy and non-intrusive method during acquisition procedure. Multiple algorithms and methods have been developed and invented by the researchers and computer scientists in order to increase and improve the performance of face recognition. Sparse representation method has attracted a lot of attention in the fields of machine learning recently and it boosts the research of sparsity-based pattern recognition among the researchers. In this research paper, we aim to investigate the impact of sparse representation in face recognition. The proposed method utilizes the fusion of Principal Component Analysis and Sparse Representation Classification to enhance the accuracy of the face recognition. Experimental results demonstrate that the proposed method can achieved almost 99% accuracy using the FERET dataset